Yingjie Xue, Xingyou Xia, Jun Zhang National University of Defense Technology, Yunbo Cao Information Center, Academy of Military Science, Dengpan Ye Guangzhou University, Guotong Geng AMS, Fei Li Wuhan University
Large Language Models (LLMs) have been widely applied in various domains such as education and healthcare, making safety assurance crucial. Jailbreak attacks, a method used in red-teaming, can help evaluate and improve the defensive strategies of LLMs. However, existing jailbreak methods often overlook the semantic differences across categories of harmful questions, leading to inconsistent success rates and reduced overall attack effectiveness. We propose the first category-aware jailbreak framework, SHARP, which incorporates the semantic category of harmful questions into prompt generation. Trained on a verified jailbreak dataset, SHARP enables the model to learn category-specific semantic features and adaptively generate prompts that bypass safety mechanisms. The method combines two-stage LoRA fine-tuning, and DPO-based reinforcement learning to optimize both attack success and category alignment. Experiments show that SHARP significantly improves attack success rates and achieves better cross-category robustness compared to the state-of-the-art (SOTA) baselines, providing an efficient and scalable tool for evaluating LLM safety.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Bo Zhang Xi’an Research Institute of High Technology, Jiawei Zhang, Cong Gao, Bingxu Han, Minghao Hu, Jun Zhang National University of Defense Technology, Yunbo Cao Information Center, Academy of Military Science, Zhunchen Luo 等 11 位作者
Although large reasoning models (LRMs) exhibit exceptional mathematical reasoning capabilities on clean inputs, their reasoning accuracy drops substantially in the presence of character-level noise such as typographical errors. Critically, their confidence estimates fail to reflect the corresponding decline in reasoning accuracy. While confidence calibration offers a principled solution, existing methods predominantly target clean inputs, leaving noisy scenarios largely unexplored. To address this gap, we propose DisCal (Distribution-aware Calibration), a confidence calibration framework for character-level noisy inputs. DisCal extracts uncertainty signals from both the empirical answer distribution and the model’s predictive distribution, and integrates them via a learned calibrator to produce well-calibrated confidence. Experiments across multiple mathematical reasoning benchmarks demonstrate that DisCal consistently outperforms existing calibration methods under noisy inputs, reducing Expected Calibration Error (ECE) by up to 39.21% and improving Area Under the Receiver Operating Characteristic Curve (AUROC) by up to 31.44%.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表